The remarkable increase in computing power together with a similar increase in sensor and actuator capabilities uow under way is enabling a significant change in how systems can sense and manipulate their environment. These changes require control algorithms capable of operating a multitude of interconnected components. In particular, novel "smart matter" systems will eventually use thousands of embedded, microsize sensors, actuators and processors. In this paper, we propose a new framework for a on-line, adaptive constrained optimization for distributed embedded applications. In this approach, on-line optimization problems are decomposed and distributed across the network, and solvers are conm311ed by an adaptive feedback mechanism that guarantees timely solutions. We also present examples from our experience in implementing smart matter systems to motivate our ideas.

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